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          Ubuntu 安装 Tensorflow-gpu
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        <blockquote>
<p><strong>更正!!!</strong></p>
<p>tensorflow1.14.0 似乎有 bug，在 NVIDIA 2070 super 上运行时会直接把显存占满，导致进程被 kill，换成 tensorflow1.15.3 后就好了，因此最终配置如下：</p>
<table>
<thead>
<tr class="header">
<th style="text-align: left;">Software</th>
<th style="text-align: left;">Version</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">nvidia driver</td>
<td style="text-align: left;">nvidia-440.82</td>
</tr>
<tr class="even">
<td style="text-align: left;">Python</td>
<td style="text-align: left;">3.6.9</td>
</tr>
<tr class="odd">
<td style="text-align: left;">tensorflow</td>
<td style="text-align: left;">tensorflow-gpu==1.15.3</td>
</tr>
<tr class="even">
<td style="text-align: left;">cuDNN</td>
<td style="text-align: left;">7.6.4</td>
</tr>
<tr class="odd">
<td style="text-align: left;">CUDA</td>
<td style="text-align: left;">10.1(V10.1.243)</td>
</tr>
</tbody>
</table>
</blockquote>
<h1 id="安装-nvidia-显卡驱动">安装 NVIDIA 显卡驱动</h1>
<ol type="1">
<li><p>下载 NVIDIA 显卡对应的<a target="_blank" rel="noopener" href="https://nvidiageforcedrivers.com/nvidia-geforce-rtx-2070-super-driver-for-linux/">驱动</a>，下载后的文件格式为 <em>.run</em></p></li>
<li><p>bios 禁用 secure boot，也就是设置为 disable</p>
<p>如果没有禁用 secure boot,会导致 NVIDIA 驱动安装失败，或者不正常。</p></li>
<li><p>禁用 nouveau 开源驱动</p>
<p>编辑 <em>/etc/modprobe.d/blacklist.conf</em> 文件，在最后加入：</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">blacklist nouveau</span></span><br></pre></td></tr></table></figure>
<p>由于nouveau是在内核中的，还需要更新一下，执行如下命令：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo update-initramfs -u</span><br></pre></td></tr></table></figure>
<p>之后重启电脑：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo reboot</span><br></pre></td></tr></table></figure>
<p>重启后查看禁用是否成功：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">lsmod | grep nouveau</span><br></pre></td></tr></table></figure>
<p>没有输出代表nouveau被禁用了</p></li>
<li><p>关闭 GUI 界面，进入命令行模式</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo telinit 3</span><br></pre></td></tr></table></figure></li>
<li><p>安装 NVIDIA 驱动</p>
<p>如果以前安装过 nvidia 驱动，需要卸载：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo apt-get autoremove –purge <span class="string">&quot;*nvidia*&quot;</span></span><br></pre></td></tr></table></figure>
<p>首先给驱动文件增加可执行权限：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo chmod a+x NVIDIA-Linux-*******.run</span><br></pre></td></tr></table></figure>
<p>然后执行安装：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo sh ./NVIDIA-Linux-*******.run -no-opengl-files</span><br></pre></td></tr></table></figure>
<p><strong>安装完成后重启 !!!</strong></p>
<blockquote>
<p><strong>–no-opengl-files</strong> 参数必须加否则会循环登录，也就是 loop login</p>
</blockquote>
<p>参数介绍：</p>
<blockquote>
<p>–no-opengl-files 只安装驱动文件，不安装 OpenGL 文件，这个参数最重要； –no-x-check 安装驱动时不检查 X服务； –no-nouveau-check 安装驱动时不检查 nouveau； 后面两个参数可不加。</p>
</blockquote></li>
<li><p>最后切换回 GUI 界面</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo telinit 5</span><br></pre></td></tr></table></figure>
<p>输入 <code>nvidia-smi</code> 查看驱动安装是否成功</p></li>
</ol>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233030.png" alt="nvidia-smi"><figcaption aria-hidden="true">nvidia-smi</figcaption>
</figure>
<p><strong>最后我装的是 430.50 版本的驱动</strong></p>
<a id="more"></a>
<h1 id="显示使用核显计算使用独显">显示使用核显，计算使用独显</h1>
<blockquote>
<p>https://forums.developer.nvidia.com/t/ubuntu-18-04-headless-390-intel-igpu-after-prime-select-intel-lost-contact-to-geforce-1050ti/66698</p>
</blockquote>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo prime-select nvidia</span><br></pre></td></tr></table></figure>
<p>add ‘nogpumanager’ kernel parameter</p>
<p>create /etc/X11/xorg.conf</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">Section <span class="string">&quot;Device&quot;</span></span><br><span class="line">    Identifier     <span class="string">&quot;intel&quot;</span></span><br><span class="line">    Driver         <span class="string">&quot;modesetting&quot;</span></span><br><span class="line">    BusID          <span class="string">&quot;PCI:0:2:0&quot;</span></span><br><span class="line">EndSection</span><br></pre></td></tr></table></figure>
<h1 id="安装-cuda-version-10.0">安装 <a target="_blank" rel="noopener" href="https://developer.nvidia.com/cuda-toolkit">CUDA</a> (version 10.0)</h1>
<p><strong>Tensorflow 与 CUDA 有对应关系</strong>，可以参考<a target="_blank" rel="noopener" href="https://www.tensorflow.org/install/source#gpu">这里</a>，主要是因为 tensorflow 会调用 <em>usr/local/cuda/lib64</em> 目录下的 <code>.so</code> 文件，我尝试过了，<code>1.14</code>,<code>1.15</code> 版本的 tensorflow 调用的都是 <code>10.0</code> 的 cuda，装错版本会提示 <code>.so</code> 文件找不到</p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233031.png" alt="version"><figcaption aria-hidden="true">version</figcaption>
</figure>
<p>我要装的是 Tensorflow-gpu 1.14.0，因此我安装 CUDA10.0 和 cuDNN7.4</p>
<p>下载 CUDA https://developer.nvidia.com/cuda-toolkit-archive</p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233032.png" alt="download"><figcaption aria-hidden="true">download</figcaption>
</figure>
<p>运行如下命令安装</p>
<figure class="highlight stata"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo <span class="keyword">sh</span> cuda_&lt;<span class="keyword">version</span>&gt;_linux.<span class="keyword">run</span></span><br></pre></td></tr></table></figure>
<p>安装过程中会有一些选项，<strong>显卡驱动不要装 !!!</strong> 因为之前已经装过了</p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233033.png" alt="installer"><figcaption aria-hidden="true">installer</figcaption>
</figure>
<p>安装完成输出的 log 会有提示</p>
<p>最后将cuda添加到系统环境中</p>
<figure class="highlight elixir"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">export LD_LIBRARY_PATH=<span class="variable">$LD_LIBRARY_PATH</span><span class="symbol">:/usr/local/cuda/lib64</span></span><br><span class="line">export PATH=<span class="variable">$PATH</span><span class="symbol">:/usr/local/cuda/bin</span></span><br><span class="line">export CUDA_HOME=<span class="variable">$CUDA_HOME</span><span class="symbol">:/usr/local/cuda</span></span><br></pre></td></tr></table></figure>
<p>检查是否安装成功：</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">nvcc -V</span></span><br></pre></td></tr></table></figure>
<h1 id="安装-cudnn-version-7.4.2">安装 cudnn (version 7.4.2)</h1>
<p>下载 <a target="_blank" rel="noopener" href="https://developer.nvidia.com/rdp/cudnn-archive#a-collapse742-10">cuDNN v7.4.2 (Dec 14, 2018), for CUDA 10.0</a></p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233034.png" alt="cudnn-download"><figcaption aria-hidden="true">cudnn-download</figcaption>
</figure>
<p>解压后，会得到一个名为 <em>cuda</em> 的文件夹，将问价拷贝到 cuda 的安装目录下</p>
<p><strong>注意!!! <em>cuda/lib64</em> 里的文件有链接的结构，如下，不能直接 cp，使用 <code>-a</code> 参数可以保持软链接结构</strong></p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233035.png" alt="la"><figcaption aria-hidden="true">la</figcaption>
</figure>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">sudo cp -a cuda/lib64/libcudnn* /usr/<span class="built_in">local</span>/cuda-10.0/lib64/</span><br><span class="line">sudo cp -a cuda/include/cudnn.h /usr/<span class="built_in">local</span>/cuda-10.0/include/</span><br></pre></td></tr></table></figure>
<p><strong>需要注意下这几个文件的权限！！！</strong></p>
<h1 id="测试-cuda-是否安装成功">测试 CUDA 是否安装成功</h1>
<ol type="1">
<li><p>切换到测试目录下：</p>
<figure class="highlight awk"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">cd <span class="regexp">/usr/</span>local<span class="regexp">/cuda-10.0/</span>samples<span class="regexp">/1_Utilities/</span>deviceQuery</span><br></pre></td></tr></table></figure></li>
<li><p>编译</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">sudo make</span></span><br></pre></td></tr></table></figure></li>
<li><p>进行测试，运行文件</p>
<figure class="highlight jboss-cli"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="string">./deviceQuery</span></span><br></pre></td></tr></table></figure>
<p>会看到类似这种结果：</p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233036.png" alt="deviceQuery"><figcaption aria-hidden="true">deviceQuery</figcaption>
</figure></li>
</ol>
<h1 id="安装多个版本的-cuda">安装多个版本的 cuda</h1>
<p>因为 cuda 安装目录下是用软链接的方式实现的，因此我们可以安装多个版本的 cuda，只要将软链接链接到对应的 cuda 就行，如下：</p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233037.png" alt="cuda"><figcaption aria-hidden="true">cuda</figcaption>
</figure>
<p>使用命令：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">rm -rf cuda <span class="comment"># 删除原来的软链接</span></span><br><span class="line">sudo ln -s /usr/<span class="built_in">local</span>/cuda-&lt;version&gt; /usr/<span class="built_in">local</span>/cuda <span class="comment"># 建立新的软链接</span></span><br></pre></td></tr></table></figure>
<p>注意环境变量的修改，可以将 cuda-<version> 修改为 cuda 这种通用形式</version></p>
<figure class="highlight elixir"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">export LD_LIBRARY_PATH=<span class="variable">$LD_LIBRARY_PATH</span><span class="symbol">:/usr/local/cuda/lib64</span></span><br><span class="line">export PATH=<span class="variable">$PATH</span><span class="symbol">:/usr/local/cuda/bin</span></span><br><span class="line">export CUDA_HOME=<span class="variable">$CUDA_HOME</span><span class="symbol">:/usr/local/cuda</span></span><br></pre></td></tr></table></figure>
<p>最后查看当前的 cuda 版本</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">nvcc -V</span></span><br></pre></td></tr></table></figure>
<p>如果确实软链接修改成功了，环境变量也改好了，版本依旧没有切换，尝试<strong>重启</strong>一下</p>
<h1 id="安装-tensorflow">安装 tensorflow</h1>
<p>参考<a target="_blank" rel="noopener" href="https://www.tensorflow.org/install/pip">使用 pip 安装 TensorFlow</a></p>
<h1 id="坑">坑！！！</h1>
<ol type="1">
<li><p><code>Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR</code></p>
<figure>
<img src="https://pic.zhouyuqian.com/img/20210727233038.png" alt="error1"><figcaption aria-hidden="true">error1</figcaption>
</figure>
<p>这个报错可能是 tensorflow 和 cuda 版本不符合，但如果已经按照<a target="_blank" rel="noopener" href="https://www.tensorflow.org/install/source#gpu">推荐列表</a>里的对应关系安装了 tensorflow 和 cuda，任然这样报错就可能是 tensorflow 占用的显存过多，进程直接被系统 kill 了，因此可以对 tensorflow 的显存进行限制。</p>
<blockquote>
<p>参考：http://www.cnblogs.com/darkknightzh/p/6591923.html</p>
</blockquote>
<ul>
<li><p>定量设置显存</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=<span class="number">0.7</span>)</span><br><span class="line">sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) </span><br></pre></td></tr></table></figure>
<p>在程序开始的地方加上这两行，这样运行TensorFlow程序时，每个使用的GPU中，占用的显存都不超过总显存的0.7。</p></li>
<li><p>按需设置显存</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">gpu_options = tf.GPUOptions(allow_growth=<span class="literal">True</span>)</span><br><span class="line">sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))   </span><br></pre></td></tr></table></figure>
<p>这样设置以后，程序就会按需占用GPU显存。</p></li>
</ul></li>
</ol>

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